Computer Science ›› 2019, Vol. 46 ›› Issue (8): 327-331.doi: 10.11896/j.issn.1002-137X.2019.08.054

• Graphics ,Image & Pattern Recognition • Previous Articles     Next Articles

Low Light Images Enhancement Based on Retinex Adaptive Reflectance Estimation and LIPS Post-processing

PAN Wei-qiong, TU Juan-juan, GAN Zong-liang, LIU Feng   

  1. (Jiangsu Province Key Lab on Image Processing and Image Communication,Nanjing University of Posts and Telecommunications,Nanjing 210003,China)
  • Received:2018-07-31 Online:2019-08-15 Published:2019-08-15

Abstract: Due to the influence of strong light,the images acquired at night have high contrast,the same situation also appears in backlit images collected in the daytime.Contrast enhancement method is usually applied to the images for obtaining images with favorable contrast.Whereas,over-enhancement commonly occurs in bright regions.Accordingly,in order to solve the problem of over-enhancement for high contrast images,a Retinex based low light image enhancement algorithm through adaptive reflection component estimation and logarithmic image processing subtraction post-proces-sing was proposed.The algorithm mainly includes into two parts:reflection component estimation and logarithmic image processing subtraction (LIPS) enhancement.First,adaptive parameter bilateral filters are used to get more accu-rate illumination layer data,instead of Gaussian filter.Moreover,the weighting estimation method is used to calculate the adaptive parameter to adjust the removal of the illumination and obtain the reflectance by just-noticeable-distortion (JND)factor.In this way,it can effectively prevent the over-enhancement in high-brightness regions.Then,the LIPS method based on maximum standard deviation of the histogram is applied to enhance reflectance component part,where the interval of the parameter is according to the cumulative distribution function (CDF).Experimental results demonstrate that the proposed method outperforms other competitive methods in terms of subjective and objective assessment

Key words: Reflectance estimation, Logarithmic image processing subtraction, Just-noticeable-distortion, Maximum standard deviation

CLC Number: 

  • TP391.41
[1] KAUR M,VERMA K.A Novel Hybrid Technique for Low Exposure Image Enhancement using Sub-image Histogram Equalization and Artificial Neural Network[C]∥International Conference on Inventive Computation Technologies.IEEE,2017:1-5.
[2] WANG S,ZHENG J,HU H,et al.Naturalness Preserved Enhancement Algorithm for Non-uniform Illumination Images [J].IEEE Transactions on Image Processing,2013,22(9):3538-3548.
[3] LI L,WANG R,WANG W,et al.A Low-light Image Enhancement Method for Both Denoising and Contrast Enlarging [C]∥2015 IEEE International Conference on Image Processing.Quebec City,Canada,2015:3730-3734.
[4] PANETTA K A,WHARTON E J,AGAIAI S S,et al.Human visual system-based image enhancement and logarithmic contrast measure [J].IEEE Transactions on Systems,Man,and Cybernetics,Part B (Cybernetics),2008,38(1):174-188.
[5] LAND EH,The Retinex [J].American Scientist,1964,52(2):247-264.
[6] JOBSON D J,RAHMAN Z,WOODELL G A,et al.Properties and Performance of a Center/surround Retinex [J].IEEE Transactions on Image Processing,1997,6(3):451-462.
[7] RAHMAN Z,JOBSON D J,WOODELL G A,et al.Multi-scale Retinex for Color Image Enhancement [C]∥Proceedings of 3rd International Conference on Image Processing.Lausanne,Swit-zerland,1996:1003-1006.
[8] JOBSON D J,RAHMAN Z,WOODELL G A,et al.A Multiscale Retinex for Bridging the Gap between Color Images and the Human Observation of Scenes [J].IEEE Transactions on Image Processing,1997,6(7):965-976.
[9] KIMMEL R,ELAD M,SHAKED D,et al.A Variational Framework for Retinex [J].International Journal of Computer Vision,2003,52(1):7-23.
[10] NG MK,WANG W.A Total Variation Model for Retinex [J].SIAM Journal on Imaging Sciences,2011,4(1):345-365.
[11] FU X,ZENG D.A Weighted Variational Model for Simultaneous Reflectance and Illumination Estimation [C]∥Proceedings of the IEEE conference on Computer Vision and Pattern Recognition.Las Vegas,NV,USA,2016:2782-2790.
[12] JOURLIN M,PINOLI J C.A Model for Logarithmic Image Processing [J].Journal of Microscopy,1988,149:21-35.
[13] JOURLIN M,PINOLI J C,ZEBOUD R,et al.Contrast Definition and Contour Detection for Logarithmic Images [J].Journal of Microscopy,1989,156(1):33-40.
[14] ZHAO Z,ZHOU Y.Comparative Study of Logarithmic Image Processing Models for Medical Image Enhancement [C]∥Proceedings of the IEEE International Conference on Systems,Man and Cybernetics.Budapest,Hungary,2016:001046-001050.
[15] HAWKES PW.Logarithmic Image Processing:Theory and Applications[M].Academic Press,2016:1-259.
[16] MEYLAN L,SUSSTRUN S.High Dynamic Range Image Rendering with A Retinex-based Adaptive Filter [J].IEEE Tran-sactions on Image Processing,2006,15(9):2820-2830
[17] XU K,JUNG C.Retinex-based Perceptual Contrast Enhance- ment in Images using Luminance Adaptation [C]∥Proceedings of the IEEE International Conference on Acoustics,Speech and Signal Processing.New Orleans,LA,USA,2017:1363-1367.
[18] TOMASI C,MANDUCHI R.Bilateral Filtering for Gray and Color Images [C]∥Proceedings of Sixth International Confe-rence on Computer Vision.Bombay,India,1998:839-846.
[19] BARTEN P G J.Contrast Sensitivity of the Human Eye and Its Effects on Image Quality [M].WA:SPIE Press,1999.
[20] JAYANT N.Signal Compression:Technology Targets and Research Directions [J].IEEE Journal on Selected Areas in Communications,1992,10(5):796-818.
[21] MITTAL A.SOUNDARARAJAN R,BOVIK AC,et al.Making a “Completely Blind” Image Quality Analyzer[J].IEEE Signal Processing Letters,2013,20(3):209-212.
[1] ZHANG Wei-feng. Spectral Reflectance Estimation by Support Vector Regression [J]. Computer Science, 2010, 37(12): 241-242.
Full text



[1] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75, 88 .
[2] SUN Qi, JIN Yan, HE Kun and XU Ling-xuan. Hybrid Evolutionary Algorithm for Solving Mixed Capacitated General Routing Problem[J]. Computer Science, 2018, 45(4): 76 -82 .
[3] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[4] WU Jian-hui, HUANG Zhong-xiang, LI Wu, WU Jian-hui, PENG Xin and ZHANG Sheng. Robustness Optimization of Sequence Decision in Urban Road Construction[J]. Computer Science, 2018, 45(4): 89 -93 .
[5] SHI Wen-jun, WU Ji-gang and LUO Yu-chun. Fast and Efficient Scheduling Algorithms for Mobile Cloud Offloading[J]. Computer Science, 2018, 45(4): 94 -99, 116 .
[6] ZHOU Yan-ping and YE Qiao-lin. L1-norm Distance Based Least Squares Twin Support Vector Machine[J]. Computer Science, 2018, 45(4): 100 -105, 130 .
[7] LIU Bo-yi, TANG Xiang-yan and CHENG Jie-ren. Recognition Method for Corn Borer Based on Templates Matching in Muliple Growth Periods[J]. Computer Science, 2018, 45(4): 106 -111, 142 .
[8] GENG Hai-jun, SHI Xin-gang, WANG Zhi-liang, YIN Xia and YIN Shao-ping. Energy-efficient Intra-domain Routing Algorithm Based on Directed Acyclic Graph[J]. Computer Science, 2018, 45(4): 112 -116 .
[9] CUI Qiong, LI Jian-hua, WANG Hong and NAN Ming-li. Resilience Analysis Model of Networked Command Information System Based on Node Repairability[J]. Computer Science, 2018, 45(4): 117 -121, 136 .
[10] WANG Zhen-chao, HOU Huan-huan and LIAN Rui. Path Optimization Scheme for Restraining Degree of Disorder in CMT[J]. Computer Science, 2018, 45(4): 122 -125 .